The Mining of Urban Hotspots Based on Multi-Source Location Data Fusion

Urban hotspots reflect the degree of residents’ travel gathering. The study of urban hotspots has important values for urban infrastructure planning, public security and other aspects. In existing researches, single-source location data and density-based clustering algorithms are used to mine hotspots. Due to the one-sidedness of using the single-source data, the mining of hotspots based on multi-source location data fusion has become a hot topic. Multi-source location data fusion requires a quantity balance between the data set to be fused, because several famous clustering algorithms cannot handle multi-source imbalanced data set. To solve this problem, we propose a novel framework to mine urban hotspots. First, we construct a data imputation model for the sparse data set so that reducing the difference in quantity between two types of data set. Then, a clustering algorithm for imbalanced data set is proposed, and a novel evaluation metric is designed to verify the effectiveness of clustering results. The experiment uses real data set including POI data, check-in data and GPS trajectory data. The results show that the proposed method discovers more than 90% of urban hotspots formed by fused imbalanced data set, and it is more accurate and efficient than the state-of-the-art algorithms.